Cite this DOI

C.Arunabala, & C.Suneetha (2018). Image Compression Using Wavelets. *Journal of Science & Technology*, *03*(01), 55–60. https://doi.org/10.46243/jstj.2018.v3.i1.204

@article{carunabala2018image,
  author    = {C.Arunabala and C.Suneetha},
  title     = {{Image Compression Using Wavelets}},
  journal   = {Journal of Science \& Technology},
  year      = {2018},
  month     = {jan},
  volume    = {03},
  number    = {01},
  pages     = {55--60},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jstj.2018.v3.i1.204},
  url       = {https://doi.org/10.46243/jstj.2018.v3.i1.204},
  language  = {en},
  abstract  = {Signal analysts already have at their disposal an impressive arsenal of tools. Perhaps the most well-known of these is Fourier analysis, which breaks down a signal into constituent sinusoids of different frequencies. Another way to think of Fourier analysis is as a mathematical technique for transforming our view of the signal from time-based to frequency-based. For many signals, Fourier analysis is extremely useful because the signal’s frequency content is ofgreat importance. Fourier analysis has a serious drawback. In transforming to the frequency domain, time information is lost. When looking at a Fourier transform of a signal, it is impossible to tellwhen a particular event took place. If the signal properties do not change much over time—that is, if it is what is called a stationary signal—this drawback isn’t very important. However, most interesting signals contain numerous non stationary or transitory characteristics: drift, trends, abrupt changes, and beginnings and ends of events. These characteristics are often the most important part of the signal, and Fourier analysis is not suited to detecting them. In an effort to correct this deficiency, Dennis Gabor (1946) adapted the Fourier transform to analyze only a small section of the signal at a time—a technique called windowing the signal. Gabor’s adaptation, called the Short-Time Fourier Transform (STFT), maps a signal into a two-dimensional function of time and frequency.The STFT represents a sort of compromise between the time-and frequency-based views of a signal. It provides some information about both when and at what frequencies a signal event occurs. However, you can only obtain this information with limited precision, and that precision is determined by the size of the window. While the STFT compromise between time and frequency information can be useful, the drawback is that once you choose a particular size for the time window, that window is the same for all frequencies. Many signals require a more flexible approach—one where we can vary the window size to determine more accurately either time or frequency}
}

TY  - JOUR
TI  - Image Compression Using Wavelets
AU  - C.Arunabala
AU  - C.Suneetha
JO  - Journal of Science & Technology
PY  - 2018
DA  - 2018/01/12/
VL  - 03
IS  - 01
SP  - 55
EP  - 60
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Signal analysts already have at their disposal an impressive arsenal of tools. Perhaps the most well-known of these is Fourier analysis, which breaks down a signal into constituent sinusoids of different frequencies. Another way to think of Fourier analysis is as a mathematical technique for transforming our view of the signal from time-based to frequency-based. For many signals, Fourier analysis is extremely useful because the signal’s frequency content is ofgreat importance. Fourier analysis has a serious drawback. In transforming to the frequency domain, time information is lost. When looking at a Fourier transform of a signal, it is impossible to tellwhen a particular event took place. If the signal properties do not change much over time—that is, if it is what is called a stationary signal—this drawback isn’t very important. However, most interesting signals contain numerous non stationary or transitory characteristics: drift, trends, abrupt changes, and beginnings and ends of events. These characteristics are often the most important part of the signal, and Fourier analysis is not suited to detecting them. In an effort to correct this deficiency, Dennis Gabor (1946) adapted the Fourier transform to analyze only a small section of the signal at a time—a technique called windowing the signal. Gabor’s adaptation, called the Short-Time Fourier Transform (STFT), maps a signal into a two-dimensional function of time and frequency.The STFT represents a sort of compromise between the time-and frequency-based views of a signal. It provides some information about both when and at what frequencies a signal event occurs. However, you can only obtain this information with limited precision, and that precision is determined by the size of the window. While the STFT compromise between time and frequency information can be useful, the drawback is that once you choose a particular size for the time window, that window is the same for all frequencies. Many signals require a more flexible approach—one where we can vary the window size to determine more accurately either time or frequency
DO  - 10.46243/jstj.2018.v3.i1.204
UR  - https://doi.org/10.46243/jstj.2018.v3.i1.204
ER  -

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    "URL": "https://doi.org/10.46243/jstj.2018.v3.i1.204",
    "title": "Image Compression Using Wavelets",
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    "author": [
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    "abstract": "Signal analysts already have at their disposal an impressive arsenal of tools. Perhaps the most well-known of these is Fourier analysis, which breaks down a signal into constituent sinusoids of different frequencies. Another way to think of Fourier analysis is as a mathematical technique for transforming our view of the signal from time-based to frequency-based. For many signals, Fourier analysis is extremely useful because the signal’s frequency content is ofgreat importance. Fourier analysis has a serious drawback. In transforming to the frequency domain, time information is lost. When looking at a Fourier transform of a signal, it is impossible to tellwhen a particular event took place. If the signal properties do not change much over time—that is, if it is what is called a stationary signal—this drawback isn’t very important. However, most interesting signals contain numerous non stationary or transitory characteristics: drift, trends, abrupt changes, and beginnings and ends of events. These characteristics are often the most important part of the signal, and Fourier analysis is not suited to detecting them. In an effort to correct this deficiency, Dennis Gabor (1946) adapted the Fourier transform to analyze only a small section of the signal at a time—a technique called windowing the signal. Gabor’s adaptation, called the Short-Time Fourier Transform (STFT), maps a signal into a two-dimensional function of time and frequency.The STFT represents a sort of compromise between the time-and frequency-based views of a signal. It provides some information about both when and at what frequencies a signal event occurs. However, you can only obtain this information with limited precision, and that precision is determined by the size of the window. While the STFT compromise between time and frequency information can be useful, the drawback is that once you choose a particular size for the time window, that window is the same for all frequencies. Many signals require a more flexible approach—one where we can vary the window size to determine more accurately either time or frequency",
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjstj.2018.v3.i1.204 gives all four in one JSON answer.